from functools import partial

import torch
import torch.nn as nn

import timm.models.vision_transformer
from timm.models.vision_transformer import Block, DropPath, Mlp

from util.pos_embed import get_2d_sincos_pos_embed

import skimage.filters.rank as sfr
from skimage.morphology import disk
import numpy as np


class PatchEmbed(nn.Module):
    """ MTR matrix to Patch Embedding
    """
    def __init__(self, img_size=40, patch_size=2, in_chans=1, embed_dim=192):
        super().__init__()
        img_size = (int(img_size / 5), img_size)
        patch_size = (patch_size, patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


class TrafficTransformer(timm.models.vision_transformer.VisionTransformer):
    def __init__(self, **kwargs):
        super(TrafficTransformer, self).__init__(**kwargs)

        self.patch_embed = PatchEmbed(img_size=kwargs['img_size'], patch_size=kwargs['patch_size'],
                                         in_chans=kwargs['in_chans'], embed_dim=kwargs['embed_dim'])

        norm_layer = kwargs['norm_layer']
        embed_dim = kwargs['embed_dim']
        self.fc_norm = norm_layer(embed_dim)
        del self.norm  # remove the original norm

    def forward_packet_features(self, x, i):
        B = x.shape[0]
        x = self.patch_embed(x)

        cls_tokens = self.cls_token.expand(B, -1, -1)

        x = torch.cat((cls_tokens, x), dim=1)
        cls_pos = self.pos_embed[:, :1, :]
        packet_pos = self.pos_embed[:, i*80+1:i*80+81, :]
        pos_all = torch.cat((cls_pos, packet_pos), dim=1)
        x = x + pos_all
        x = self.pos_drop(x)

        for blk in self.blocks:
            x = blk(x)

        cls = x[:, :1, :]

        x = x[:, 1:, :]
        x = x.reshape(B, 4, 20, -1).mean(axis=1)
        x = torch.cat((cls, x), dim=1)

        self.fc_norm(x)

        return x

    def forward_features(self, x):
        B, C, H, W = x.shape
        x = x.reshape(B, C, 5, -1)
        for i in range(5):
            packet_x = x[:, :, i, :]
            packet_x = packet_x.reshape(B, C, -1, 40)
            packet_x = self.forward_packet_features(packet_x, i)
            if i == 0:
                new_x = packet_x
            else:
                new_x = torch.cat((new_x, packet_x), dim=1)
        x = new_x

        for blk in self.blocks:
            x = blk(x)

        x = x.reshape(B, 5, 21, -1)[:, :, 0, :]
        x = x.mean(dim=1)

        outcome = self.fc_norm(x)
        return outcome


class MaskedAutoencoder(nn.Module):
    """ Masked Autoencoder
    """

    def __init__(self, img_size=40, patch_size=2, in_chans=1,
                 embed_dim=192, depth=4, num_heads=16,
                 decoder_embed_dim=128, decoder_depth=2, decoder_num_heads=16,
                 mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False):
        super().__init__()

        # --------------------------------------------------------------------------
        # MAE encoder specifics
        self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim)
        self.num_patches = self.patch_embed.num_patches * 5

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, self.num_patches + 1, embed_dim),
                                      requires_grad=False)  # fixed sin-cos embedding

        self.blocks = nn.ModuleList([
            Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer)
            for i in range(depth)])
        self.norm = norm_layer(embed_dim)
        # --------------------------------------------------------------------------

        # --------------------------------------------------------------------------
        # MAE decoder specifics
        self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)

        self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))

        self.decoder_pos_embed = nn.Parameter(torch.zeros(1, self.num_patches + 1, decoder_embed_dim),
                                              requires_grad=False)  # fixed sin-cos embedding

        self.decoder_blocks = nn.ModuleList([
            Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer)
            for i in range(decoder_depth)])

        self.decoder_norm = norm_layer(decoder_embed_dim)
        self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size ** 2 * in_chans, bias=True)  # decoder to patch
        # --------------------------------------------------------------------------

        self.norm_pix_loss = norm_pix_loss

        self.initialize_weights()

    def initialize_weights(self):
        # initialization
        # initialize (and freeze) pos_embed by sin-cos embedding
        pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.num_patches ** .5),
                                            cls_token=True)
        self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))

        decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1],
                                                    int(self.num_patches ** .5), cls_token=True)
        self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0))

        # initialize patch_embed like nn.Linear (instead of nn.Conv2d)
        w = self.patch_embed.proj.weight.data
        torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))

        # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
        torch.nn.init.normal_(self.cls_token, std=.02)
        torch.nn.init.normal_(self.mask_token, std=.02)

        # initialize nn.Linear and nn.LayerNorm
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            # we use xavier_uniform following official JAX ViT:
            torch.nn.init.xavier_uniform_(m.weight)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def patchify(self, imgs):
        """
        imgs: (N, 1, H, W)
        x: (N, L, patch_size**2 *1)
        """
        p = self.patch_embed.patch_size[0]
        assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0

        h = w = imgs.shape[2] // p
        x = imgs.reshape(shape=(imgs.shape[0], 1, h, p, w, p))
        x = torch.einsum('nchpwq->nhwpqc', x)
        x = x.reshape(shape=(imgs.shape[0], h * w, p ** 2 * 1))
        return x

    def unpatchify(self, x):
        """
        x: (N, L, patch_size**2 *1)
        imgs: (N, 1, H, W)
        """
        p = self.patch_embed.patch_size[0]
        h = w = int(x.shape[1] ** .5)
        assert h * w == x.shape[1]

        x = x.reshape(shape=(x.shape[0], h, w, p, p, 1))
        x = torch.einsum('nhwpqc->nchpwq', x)
        imgs = x.reshape(shape=(x.shape[0], 1, h * p, h * p))
        return imgs

    def random_masking(self, x, mask_ratio):
        """
        Perform per-sample random masking by per-sample shuffling.
        Per-sample shuffling is done by argsort random noise.
        x: [N, L, D], sequence
        """
        N, L, D = x.shape  # batch, length, dim
        len_keep = int(L * (1 - mask_ratio))

        noise = torch.rand(N, L, device=x.device)  # noise in [0, 1]

        # sort noise for each sample
        ids_shuffle = torch.argsort(noise, dim=1)  # ascend: small is keep, large is remove
        ids_restore = torch.argsort(ids_shuffle, dim=1)

        # keep the first subset
        ids_keep = ids_shuffle[:, :len_keep]
        x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))

        # generate the binary mask: 0 is keep, 1 is remove
        mask = torch.ones([N, L], device=x.device)
        mask[:, :len_keep] = 0
        # unshuffle to get the binary mask
        mask = torch.gather(mask, dim=1, index=ids_restore)

        return x_masked, mask, ids_restore

    def forward_encoder(self, x, mask_ratio):
        # embed patches
        x = self.patch_embed(x)

        # add pos embed w/o cls token
        x = x + self.pos_embed[:, 1:, :]

        # masking: length -> length * mask_ratio
        x, mask, ids_restore = self.random_masking(x, mask_ratio)

        # append cls token
        cls_token = self.cls_token + self.pos_embed[:, :1, :]
        cls_tokens = cls_token.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_tokens, x), dim=1)

        # apply Transformer blocks
        for blk in self.blocks:
            x = blk(x)
        x = self.norm(x)

        return x, mask, ids_restore

    def forward_decoder(self, x, ids_restore):
        # embed tokens
        x = self.decoder_embed(x)

        # append mask tokens to sequence
        mask_tokens = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] + 1 - x.shape[1], 1)
        x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1)  # no cls token
        x_ = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]))  # unshuffle
        x = torch.cat([x[:, :1, :], x_], dim=1)  # append cls token

        # add pos embed
        x = x + self.decoder_pos_embed

        # apply Transformer blocks
        for blk in self.decoder_blocks:
            x = blk(x)
        x = self.decoder_norm(x)

        # predictor projection
        x = self.decoder_pred(x)

        # remove cls token
        x = x[:, 1:, :]

        return x

    def forward_loss(self, imgs, pred, mask):
        """
        imgs: [N, 1, H, W]
        pred: [N, L, p*p*1]
        mask: [N, L], 0 is keep, 1 is remove,
        """
        target = self.patchify(imgs)
        if self.norm_pix_loss:
            mean = target.mean(dim=-1, keepdim=True)
            var = target.var(dim=-1, keepdim=True)
            target = (target - mean) / (var + 1.e-6) ** .5

        loss = (pred - target) ** 2
        loss = loss.mean(dim=-1)  # [N, L], mean loss per patch

        loss = (loss * mask).sum() / mask.sum()  # mean loss on removed patches
        return loss

    def forward(self, imgs, mask_ratio=0.75):
        latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio)
        pred = self.forward_decoder(latent, ids_restore)
        loss = self.forward_loss(imgs, pred, mask)
        return loss, pred, mask


# for pre-training
def MAE_YaTC(**kwargs):
    model = MaskedAutoencoder(
        img_size=40, patch_size=2, embed_dim=192, depth=4, num_heads=16,
        decoder_embed_dim=128, decoder_depth=2, decoder_num_heads=16,
        mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model


# for fine-tuning
def TraFormer_YaTC(**kwargs):
    model = TrafficTransformer(
        img_size=40, patch_size=2, in_chans=1, embed_dim=192, depth=4, num_heads=16, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    return model